Investigation on the performance of meta-heuristics for solving single objective conceptual design of a conventional fixed wing unmanned aerial vehicle
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Abstract
In this work, conceptual design optimisation of a conventional fixed wing unmanned aerial vehicle (UAV) is performed through metaheuristics. Five optimisation objective functions including take-off gross weight , take off distance , endurance , lift coefficient and drag coefficient at cruising are scalarised into a single-objective optimisation problem subject to aircraft flight mission, performance, and stability constraints. Aerodynamic and stability analyses are executed by a vortex lattice method (VLM) while aircraft component weights and aircraft performance are estimated by empirical equations. Six state-of-the-art of single-objective meta-heuristics (MH) including Equilibrium Optimizer (EO), Evolution Strategies algorithm (ES), Moth-Flame Optimization Algorithm (MFO), Marine Predators Algorithm (MPA), Slime Mould Algorithm (SMA), and Salp Swarm Algorithm (SSA) are employed to solve the problem while their search performance are statistically investigated based on the Friedman test. The results obtained shown that the best and second-best optimiser are EO and MFA, respectively. Based on this study, the optimal result which can be chosen for further design stages (preliminary and detail design) is revealed.
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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